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I am a computational linguist. My research models both (i) the emergence of grammatical structure within individuals and (ii) variation in these structures across populations. To support this work, I've developed large multi-lingual geographic corpora that support a broad range of natural experiments. My recent work has also focused on (i) the impact that linguistic variation has on natural language processing and (ii) on low-resource dialects and languages. For example, I recently received a grant to study how machine-assisted writing environments alter the production of both individuals and the larger populations they belong to.
I have published nearly 40 papers across linguistics and computational linguistics and my first book was recently published by Cambridge University Press. My research has been generously supported by the Royal Society's Marsden Fund (NZ), by the Science for Technological Innovation program at New Zealand's National Science Challenges fund, by the National Geospatial-Intelligence Agency, and by the ODNI. I've also had the opportunity to create a MOOC which has now taught over 14,000 students about NLP, part of my efforts to make sure that linguists have access to work in computational linguistics.
If you are a student interested in studying computational linguistics at Illinois, feel free to send me an email!
Highlighted Publications
Dunn, J. E. (2024). Computational Construction Grammar: A Usage-Based Approach. (Elements in Cognitive Linguistics). Cambridge University Press. https://doi.org/10.1017/9781009233743
Dunn, J. (2022). Natural Language Processing for Corpus Linguistics. (Elements in Corpus Linguistics). Cambridge University Press. https://doi.org/10.1017/9781009070447
Dunn, J. E. (2023). Syntactic variation across the grammar: Modelling a complex adaptive system. Frontiers in Complex Systems, 1, Article 1273741. https://doi.org/10.3389/fcpxs.2023.1273741
Recent Publications
Dunn, J. E. (2024). Computational Construction Grammar: A Usage-Based Approach. (Elements in Cognitive Linguistics). Cambridge University Press. https://doi.org/10.1017/9781009233743
Dunn, J. E., & Edwards-Brown, L. (2024). Geographically-Informed Language Identification. In Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Dunn, J., & Edwards-Brown, L. (2024). Geographically-Informed Language Identification. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 7672-7682). (2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings). European Language Resources Association (ELRA).
Dunn, J. E., Adams, B., & Madabushi, H. T. (2024). Pre-Trained Language Models Represent Some Geographic Populations Better Than Others. In Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Dunn, J., Adams, B., & Madabushi, H. T. (2024). Pre-Trained Language Models Represent Some Geographic Populations Better Than Others. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings (pp. 12966-12976). (2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings). European Language Resources Association (ELRA).